Designing the first risk management tool at Amazon — turning a scattered, spreadsheet-driven process into a single system teams actually trust.
Before this project, risk tracking happened wherever each team could manage it — spreadsheets, wiki pages, email threads. There was no shared definition of what a "risk" was, no consistent severity scale, and no way for leadership to see aggregate exposure across orgs.
That created three compounding problems: risks were logged inconsistently, ownership was ambiguous, and the same issue got reported multiple times by different teams. Leadership was making decisions on an incomplete picture.
How do we create one system that is structured enough to aggregate risk data reliably, but flexible enough that a dozen very different teams will actually adopt it?
I interviewed 14 people across three roles — risk owners who log issues, managers who triage them, and leaders who report upward. I also audited 20+ existing spreadsheets to understand what people were actually tracking versus what the official process asked for.
The instinct was to mandate a rigid taxonomy. But research showed that every previous attempt at enforcement had failed — people simply worked around it. The insight that reframed the project: if logging a risk is faster than not logging it, structure becomes a byproduct rather than a burden.
So instead of a long compliance form, I designed a progressive flow — capture the minimum viable risk in under a minute, then enrich it later as triage progresses. Structure emerges through the workflow rather than being demanded upfront.
I explored three information architectures: a linear queue, a severity-first matrix, and an ownership-based dashboard. Testing showed the dashboard model matched how managers actually think — they scan by ownership first, then drill into severity.
I ran moderated usability sessions with 8 participants on an interactive prototype. Two changes came directly out of those sessions: severity became a guided set of questions rather than a free choice, and duplicate detection surfaced inline while typing instead of after submission.
A single-screen entry flow that asks only what's needed to route a risk. Inline duplicate detection catches overlaps as the user types, cutting redundant entries significantly.
Rather than asking users to pick a number, the system asks about impact and likelihood in plain language and derives severity consistently. This made ratings comparable across teams for the first time.
Managers see their team's exposure grouped by owner with trend indicators over time. Leadership gets the same data rolled up, so conversations start from a shared source of truth.
Note: metrics shown are directional and rounded. Detailed figures are confidential.
I under-invested in the triage experience early on. I focused on capture because that was the loudest complaint, but managers turned out to be the group whose time was most wasted. If I ran this again, I'd map the full lifecycle before choosing where to start.
The lesson that stuck: adoption is a design problem, not a rollout problem. Every place we replaced a mandate with a genuinely faster path, usage followed on its own.